Contrast Enhanced Magnetic Resonance Angiography (CEMRA) with gadolinium (Gd) provides high resolution visualization of the vascular compartment, but is rarely used in patients with advanced chronic kidney disease (CKD) (estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73m2) due to the risk of nephrogenic systemic fibrosis (NSF). There is thus an unmet need to develop MRI techniques that can both replace Gd and provide new insights into the etiology and severity of CKD. PI Sridhar and his team at Northeastern University (NEU) have developed a new technique, Quantitative Ultra-short Time-to-Echo Contrast-Enhanced (QUTE-CE) MRI, leads to quantifiable positive contrast images of the vasculature with unprecedented clarity and definition. QUTE-CE is particularly optimal using Ferumoxytol (Feraheme), an FDA approved iron-oxide nanopharmaceutical, that is already routinely used for iron-deficient anemia therapy in CKD patients. Under an ongoing clinical trial (NCT03266848) we have demonstrated QUTE- CE MRI for renal and cerebral imaging in humans at 3T. This R21 project will develop the QUTE-CE MRI method for kidney imaging in patients with both normal kidney function as well as with CKD. We propose to scan 25 total patients (10 in Year 1 and 15 in Year 2) who are already scheduled to receive ferumoxytol infusion for iron-deficiency anemia therapy. The specific aims of this project are summarized below. Specific Aim 1: Renal Vascular Angiograms of CKD Patients. These studies will be conducted at NEU and Massachusetts General Hospital (MGH) using Siemens Prisma MRI scanners. Three tasks will be pursued to develop the optimizing protocol. Task 1: Develop a robust QUTE-CE imaging protocol. Task 2: Implement a robust methodology for accounting for B1 inhomogeneity. Task 3: Develop a robust trajectory for improved image reconstruction including motion correction. We hypothesize that QUTE-CE MRI will lead to high resolution angiograms that will allow for the safe diagnosis of existing pathologies such as renal artery stenosis in addition to the novel identification of kidney micro-vascular disease. Specific Aim 2: Obtain quantitative renal blood volume (RBV) maps and develop machine learning algorithms (MLA) to better understand the severity of kidney disease. The renal angiograms will be analyzed to obtain renal blood volume (RBV) maps. The RBV maps will be analyzed in terms of Machine-Learning algorithms (MLA) to explore whether we can identify common etiologies of CKD as well as estimate the severity of kidney disease. The MLA will enable organ segmentation, automatized renal parenchyma volumetry, as well as automated extraction and labeling of lesions. The MLA results will be compared with radiological scoring, estimated GFR, and degree of albuminuria. We hypothesize that the absolute RBV maps will enable quantitative monitoring of blood volume in cortex and medulla, and that RBV could be a surrogate parameter for renal microvascular rarefaction, a central mechanism in CKD initiation and progression.